Ryan J Urbanowicz
Zitiert von
Zitiert von
Relief-based feature selection: Introduction and review
RJ Urbanowicz, M Meeker, W La Cava, RS Olson, JH Moore
Journal of biomedical informatics 85, 189-203, 2018
Evaluation of a tree-based pipeline optimization tool for automating data science
RS Olson, N Bartley, RJ Urbanowicz, JH Moore
Proceedings of the genetic and evolutionary computation conference 2016, 485-492, 2016
Learning classifier systems: a complete introduction, review, and roadmap
RJ Urbanowicz, JH Moore
Journal of Artificial Evolution and Applications 2009, 2009
Automating biomedical data science through tree-based pipeline optimization
RS Olson, RJ Urbanowicz, PC Andrews, NA Lavender, LC Kidd, ...
European conference on the applications of evolutionary computation, 123-137, 2016
PMLB: a large benchmark suite for machine learning evaluation and comparison
RS Olson, W La Cava, P Orzechowski, RJ Urbanowicz, JH Moore
BioData mining 10 (1), 1-13, 2017
GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures
RJ Urbanowicz, J Kiralis, NA Sinnott-Armstrong, T Heberling, JM Fisher, ...
BioData mining 5 (1), 1-14, 2012
Benchmarking relief-based feature selection methods for bioinformatics data mining
RJ Urbanowicz, RS Olson, P Schmitt, M Meeker, JH Moore
Journal of biomedical informatics 85, 168-188, 2018
ExSTraCS 2.0: description and evaluation of a scalable learning classifier system
RJ Urbanowicz, JH Moore
Evolutionary intelligence 8 (2), 89-116, 2015
Introduction to learning classifier systems
RJ Urbanowicz, WN Browne
Springer, 2017
Analysis of gene‐gene interactions
D Gilbert‐Diamond, JH Moore
Current protocols in human genetics 70 (1), 1.14. 1-1.14. 12, 2011
Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach
RJ Urbanowicz, AS Andrew, MR Karagas, JH Moore
Journal of the American Medical Informatics Association 20 (4), 603-612, 2013
An analysis pipeline with statistical and visualization-guided knowledge discovery for michigan-style learning classifier systems
RJ Urbanowicz, A Granizo-Mackenzie, JH Moore
IEEE computational intelligence magazine 7 (4), 35-45, 2012
The application of michigan-style learning classifiersystems to address genetic heterogeneity and epistasisin association studies
RJ Urbanowicz, JH Moore
Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010
Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems
R Urbanowicz, A Granizo-Mackenzie, J Moore
Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012
Statistical inference Relief (STIR) feature selection
TT Le, RJ Urbanowicz, JH Moore, BA McKinney
Bioinformatics 35 (8), 1358-1365, 2019
Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection
RJ Urbanowicz, J Kiralis, JM Fisher, JH Moore
BioData mining 5 (1), 1-13, 2012
Using expert knowledge to guide covering and mutation in a michigan style learning classifier system to detect epistasis and heterogeneity
RJ Urbanowicz, D Granizo-Mackenzie, JH Moore
International Conference on Parallel Problem Solving from Nature, 266-275, 2012
Rapid rule compaction strategies for global knowledge discovery in a supervised learning classifier system
J Tan, J Moore, R Urbanowicz
ECAL 2013: The Twelfth European Conference on Artificial Life, 110-117, 2013
Preparing next-generation scientists for biomedical big data: artificial intelligence approaches
JH Moore, MR Boland, PG Camara, H Chervitz, G Gonzalez, BE Himes, ...
Personalized medicine 16 (3), 247-257, 2019
A system for accessible artificial intelligence
RS Olson, M Sipper, WL Cava, S Tartarone, S Vitale, W Fu, ...
Genetic programming theory and practice XV, 121-134, 2018
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